At times a sample may indicate that the auditor's assessed level of control risk for a given control is reasonable when, in fact, the true compliance rate does not justify the assessed level. This situation illustrates the risk of

At times a sample may indicate that the auditor's assessed level of control risk for a given control is reasonable when, in fact, the true compliance rate does not justify the assessed level. This situation illustrates the risk of

• A. assessing control risk too low
• B. incorrect precision
• C. assessing control risk too high
• D. incorrect rejection

Answer: (A)

This item centers on the idea that sampling can mislead about how well a control works. If the auditor’s sample suggests the control is operating effectively and the assessed level of control risk is reasonable, but the true rate of compliance in the population is actually low, the auditor has under- or underestimated the level of control risk. Relying on a control that isn’t as effective as believed increases the chance that misstatements slip through, because the planned substantive procedures may be too light. This is a classic example of sampling risk leading to assessing the control risk too low. It’s about the mismatch between what the sample supports and the reality in the population, not about overestimating risk, printing the wrong precision, or a different type of error like incorrect rejection.

This item centers on the idea that sampling can mislead about how well a control works. If the auditor’s sample suggests the control is operating effectively and the assessed level of control risk is reasonable, but the true rate of compliance in the population is actually low, the auditor has under- or underestimated the level of control risk. Relying on a control that isn’t as effective as believed increases the chance that misstatements slip through, because the planned substantive procedures may be too light. This is a classic example of sampling risk leading to assessing the control risk too low. It’s about the mismatch between what the sample supports and the reality in the population, not about overestimating risk, printing the wrong precision, or a different type of error like incorrect rejection.